Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks

• URL: http://arxiv.org/abs/2303.11338v3
• Date: Mon, 3 Jul 2023 07:39:48 GMT
• Title: Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks
• Authors: Aristotelis Ballas and Christos Diou
• Abstract summary: Domain Generalization problem for biosignals focuses on electrocardiograms (ECG) and electroencephalograms (EEG) This paper proposes a benchmark for evaluating DG algorithms and introduces a novel architecture for tackling DG in biosignal classification. To our knowledge, this is the first attempt at developing an open-source framework for evaluating ECG and EEG DG algorithms.
• Score: 3.1372269816123994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite their immense success in numerous fields, machine and deep learning systems have not yet been able to firmly establish themselves in mission-critical applications in healthcare. One of the main reasons lies in the fact that when models are presented with previously unseen, Out-of-Distribution samples, their performance deteriorates significantly. This is known as the Domain Generalization (DG) problem. Our objective in this work is to propose a benchmark for evaluating DG algorithms, in addition to introducing a novel architecture for tackling DG in biosignal classification. In this paper, we describe the Domain Generalization problem for biosignals, focusing on electrocardiograms (ECG) and electroencephalograms (EEG) and propose and implement an open-source biosignal DG evaluation benchmark. Furthermore, we adapt state-of-the-art DG algorithms from computer vision to the problem of 1D biosignal classification and evaluate their effectiveness. Finally, we also introduce a novel neural network architecture that leverages multi-layer representations for improved model generalizability. By implementing the above DG setup we are able to experimentally demonstrate the presence of the DG problem in ECG and EEG datasets. In addition, our proposed model demonstrates improved effectiveness compared to the baseline algorithms, exceeding the state-of-the-art in both datasets. Recognizing the significance of the distribution shift present in biosignal datasets, the presented benchmark aims at urging further research into the field of biomedical DG by simplifying the evaluation process of proposed algorithms. To our knowledge, this is the first attempt at developing an open-source framework for evaluating ECG and EEG DG algorithms.

Related papers

• Disentangling Masked Autoencoders for Unsupervised Domain Generalization [57.56744870106124]
  Unsupervised domain generalization is fast gaining attention but is still far from well-studied. Disentangled Masked Auto (DisMAE) aims to discover the disentangled representations that faithfully reveal intrinsic features. DisMAE co-trains the asymmetric dual-branch architecture with semantic and lightweight variation encoders.
  arXiv  Detail & Related papers  (2024-07-10T11:11:36Z)
• ECGMamba: Towards Efficient ECG Classification with BiSSM [3.0120310355085467]
  We propose a novel model, ECGMamba, which employs a bidirectional state-space model (BiSSM) to enhance classification efficiency. The experimental results on two publicly available ECG datasets demonstrate that ECGMamba effectively balances the effectiveness and efficiency of classification.
  arXiv  Detail & Related papers  (2024-06-14T14:55:53Z)
• hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications [3.031375888004876]
  Two main issues challenge the existing DL-based modeling methods for EEG. High variability between subjects and low signal-to-noise ratio make it difficult to ensure a good quality in the EEG data. We propose two variational autoencoder models, namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG reconstruction.
  arXiv  Detail & Related papers  (2023-11-20T15:36:31Z)
• Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis [4.3312979375047025]
  This paper proposes an unsupervised pre-training technique based on masked autoencoder (MAE) for electrocardiogram (ECG) signals. In addition, we propose a task-specific fine-tuning to form a complete framework for ECG analysis. The framework is high-level, universal, and not individually adapted to specific model architectures or tasks.
  arXiv  Detail & Related papers  (2023-10-17T11:19:51Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• Leveraging Statistical Shape Priors in GAN-based ECG Synthesis [3.3482093430607267]
  We propose a novel approach for ECG signal generation using Generative Adversarial Networks (GANs) and statistical ECG data modeling. Our approach leverages prior knowledge about ECG dynamics to synthesize realistic signals, addressing the complex dynamics of ECG signals. Our results demonstrate that our approach, which models temporal and amplitude variations of ECG signals as 2-D shapes, generates more realistic signals compared to state-of-the-art GAN based generation baselines.
  arXiv  Detail & Related papers  (2022-10-22T18:06:11Z)
• On Certifying and Improving Generalization to Unseen Domains [87.00662852876177]
  Domain Generalization aims to learn models whose performance remains high on unseen domains encountered at test-time. It is challenging to evaluate DG algorithms comprehensively using a few benchmark datasets. We propose a universal certification framework that can efficiently certify the worst-case performance of any DG method.
  arXiv  Detail & Related papers  (2022-06-24T16:29:43Z)
• Generalized Visual Quality Assessment of GAN-Generated Face Images [79.47386781978531]
  We study the subjective and objective quality towards generalized quality assessment of GAN-generated face images (GFIs) We develop a quality assessment model that is able to deliver accurate quality predictions for GFIs from both available and unseen GAN algorithms.
  arXiv  Detail & Related papers  (2022-01-28T07:54:49Z)
• A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources [79.48829365560788]
  Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios. HG embedding aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks.
  arXiv  Detail & Related papers  (2020-11-30T15:03:47Z)
• Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL [15.552721021992847]
  We put forward first benchmarking results for the recently published, freely accessible PTB-XL dataset. We find that convolutional neural networks, in particular resnet- and inception-based architectures, show the strongest performance across all tasks. Results are complemented by deeper insights into the classification algorithm in terms of hidden stratification, model uncertainty and an exploratory interpretability analysis.
  arXiv  Detail & Related papers  (2020-04-28T17:55:17Z)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.